Red wine quality EDA
Taef al khales
- 1. The Data
- 2. EDA
- 2.1 First look at the data
- 2.2 Tidy up the columns names
- 2.3 Summary statistics
- 2.4 Univariate
- 2.4.1 Quality Distribution
- 2.4.2 PH Distribution (boxplot)
- 2.4.3 All data Distribution
- 2.4.2 Alcohol Distribution (boxplot)
- 2.5 Bivariate
- 2.5.1 GGPair graph (group by quality)
- 2.5.2 Correlation (heatmap)
- 2.6 Mean values grouped by the quality
- 2.7 2D interractive graph
- 3. Conclusion
1. The Data
- fixed acidity: most acids involved with wine or fixed or nonvolatile.
- volatile acidity:the amount of acetic acid in wine, which at too high of levels can lead to an unpleasant, vinegar taste
- citric acid:found in small quantities, citric acid can add ‘freshness’ and flavor to wines
- residual sugar: the amount of sugar remaining after fermentation stops, it’s rare to find wines with less than 1 gram/liter and wines with greater than 45 grams/liter are considered sweet
- chlorides: the amount of salt in the wine
- free sulfur dioxide : the free form of SO2 exists in equilibrium between molecular SO2 (as a dissolved gas) and bisulfite ion
- total sulfur dioxide: amount of free and bound forms of S02; in low concentrations
- density: the density of water is close to that of water depending on the percent alcohol and sugar content
- pH:describes how acidic or basic a wine is on a scale from 0 (very acidic) to 14 (very basic); most wines are between 3-4 on the pH scale
- sulphates: a wine additive which can contribute to sulfur dioxide gas (S02) levels, wich acts as an antimicrobial and antioxidant
- alcohol:the percent alcohol content of the wine
- quality (score between 0 and 10)
2. EDA
2.1 First look at the data
| fixed acidity | volatile acidity | citric acid | residual sugar | chlorides | free sulfur dioxide | total sulfur dioxide | density | pH | sulphates | alcohol | quality |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 7.4 | 0.70 | 0.00 | 1.9 | 0.076 | 11 | 34 | 0.9978 | 3.51 | 0.56 | 9.4 | 5 |
| 7.8 | 0.88 | 0.00 | 2.6 | 0.098 | 25 | 67 | 0.9968 | 3.20 | 0.68 | 9.8 | 5 |
| 7.8 | 0.76 | 0.04 | 2.3 | 0.092 | 15 | 54 | 0.9970 | 3.26 | 0.65 | 9.8 | 5 |
| 11.2 | 0.28 | 0.56 | 1.9 | 0.075 | 17 | 60 | 0.9980 | 3.16 | 0.58 | 9.8 | 6 |
| 7.4 | 0.70 | 0.00 | 1.9 | 0.076 | 11 | 34 | 0.9978 | 3.51 | 0.56 | 9.4 | 5 |
| 7.4 | 0.66 | 0.00 | 1.8 | 0.075 | 13 | 40 | 0.9978 | 3.51 | 0.56 | 9.4 | 5 |
2.2 Tidy up the columns names
Now the names are seprated with (_) instead of a space. and be in lower case.
2.3 Summary statistics
Skim is a very nice package for summary statistics Skim.
skim(wine_clean)| Name | wine_clean |
| Number of rows | 1599 |
| Number of columns | 12 |
| _______________________ | |
| Column type frequency: | |
| numeric | 12 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| fixed_acidity | 0 | 1 | 8.32 | 1.74 | 4.60 | 7.10 | 7.90 | 9.20 | 15.90 | ▂▇▂▁▁ |
| volatile_acidity | 0 | 1 | 0.53 | 0.18 | 0.12 | 0.39 | 0.52 | 0.64 | 1.58 | ▅▇▂▁▁ |
| citric_acid | 0 | 1 | 0.27 | 0.19 | 0.00 | 0.09 | 0.26 | 0.42 | 1.00 | ▇▆▅▁▁ |
| residual_sugar | 0 | 1 | 2.54 | 1.41 | 0.90 | 1.90 | 2.20 | 2.60 | 15.50 | ▇▁▁▁▁ |
| chlorides | 0 | 1 | 0.09 | 0.05 | 0.01 | 0.07 | 0.08 | 0.09 | 0.61 | ▇▁▁▁▁ |
| free_sulfur_dioxide | 0 | 1 | 15.87 | 10.46 | 1.00 | 7.00 | 14.00 | 21.00 | 72.00 | ▇▅▁▁▁ |
| total_sulfur_dioxide | 0 | 1 | 46.47 | 32.90 | 6.00 | 22.00 | 38.00 | 62.00 | 289.00 | ▇▂▁▁▁ |
| density | 0 | 1 | 1.00 | 0.00 | 0.99 | 1.00 | 1.00 | 1.00 | 1.00 | ▁▃▇▂▁ |
| p_h | 0 | 1 | 3.31 | 0.15 | 2.74 | 3.21 | 3.31 | 3.40 | 4.01 | ▁▅▇▂▁ |
| sulphates | 0 | 1 | 0.66 | 0.17 | 0.33 | 0.55 | 0.62 | 0.73 | 2.00 | ▇▅▁▁▁ |
| alcohol | 0 | 1 | 10.42 | 1.07 | 8.40 | 9.50 | 10.20 | 11.10 | 14.90 | ▇▇▃▁▁ |
| quality | 0 | 1 | 5.64 | 0.81 | 3.00 | 5.00 | 6.00 | 6.00 | 8.00 | ▁▇▇▂▁ |
We can see the following things:
- all columns are numeric
- no missing values
- it is not a big dataset
2.4 Univariate
2.4.1 Quality Distribution
It appears that is this sample dataset, the most frequent wine quality are 5 and 6.
2.4.2 PH Distribution (boxplot)
2.4.3 All data Distribution
2.4.2 Alcohol Distribution (boxplot)
2.5 Bivariate
2.5.1 GGPair graph (group by quality)
wine_ggpair <- wine_clean
wine_ggpair$quality <- as.factor(wine_ggpair$quality)
ggpairs(wine_ggpair,columns = 1:6,aes(colour = quality, alpha = 0.4), upper=list(continuous = "blank"))
wine_ggpair2 <- wine_clean
wine_ggpair2$quality <- as.factor(wine_ggpair2$quality)
ggpairs(wine_ggpair2,columns = 7:11,aes(colour = quality, alpha = 0.4), upper=list(continuous = "blank"))
2.5.2 Correlation (heatmap)
- Alcohol and Quality are inversely highly correlated.
- fixed acidity is highly correlated with Ensity and Citric acid
- total sulfur dioxide is highly correlated with free sulfur dioxide
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2.6 Mean values grouped by the quality
wine_df <- wine_clean %>% group_by(quality) %>% summarise(across(everything(), mean))
wine_df <- signif(wine_df, digits = 3)
reactable(wine_df)2.7 2D interractive graph
plot <- ggplot(data=wine_clean, aes(alcohol,fixed_acidity)) +
geom_point(aes(color=quality)) +
ggtitle("2D interractive graph")
ggplotly(plot)3. Conclusion
In this report i have done some EDA for the Red Wine Quality from Kaggle. And it shows that some of the features like Alcohol and fixed_acidity may be good to build a prediction models the quality of the wine. And based on the heat map there were some inverse correlation between the values.